Downloads · 30 days
4
4% of all-time downloads
vi-meeting-segmentation
neyugn/vi-meeting-segmentation
vi-meeting-segmentation is a machine learning model from neyugn. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
94
Public
Parameters
1.5M
118 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors5.9 MB · 100%
At a glance
- Library
- transformers
- License
- mit
- Model type
- pyannet
- Access
- Public
- Created
- Jun 3, 2026
- Updated
- Jun 3, 2026
- SHA
- 2e0e9478
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
vi-meeting-segmentation
This model is a fine-tuned version of pyannote/segmentation-3.0 on the neyugn/synthetic-diarization-vi-meeting-4-5spk dataset. It achieves the following results on the evaluation set:
- Loss: 0.5450
- Model Preparation Time: 0.0039
- Der: 0.1912
- False Alarm: 0.0537
- Missed Detection: 0.0352
- Confusion: 0.1023
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|---|---|---|---|---|---|---|---|---|
| 0.6062 | 1.0 | 132 | 0.6128 | 0.0039 | 0.2110 | 0.0521 | 0.0469 | 0.1121 |
| 0.5287 | 2.0 | 264 | 0.5575 | 0.0039 | 0.1987 | 0.0541 | 0.0392 | 0.1054 |
| 0.4709 | 3.0 | 396 | 0.5482 | 0.0039 | 0.1911 | 0.0520 | 0.0385 | 0.1006 |
| 0.4252 | 4.0 | 528 | 0.5501 | 0.0039 | 0.1914 | 0.0535 | 0.0355 | 0.1024 |
| 0.4234 | 5.0 | 660 | 0.5450 | 0.0039 | 0.1912 | 0.0537 | 0.0352 | 0.1023 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
- Library
- transformers
- Type
- pyannet
- License
- mit
- Created
- Jun 3, 2026
- Updated
- Jun 3, 2026